Asthma identification through multimodal data fusion: towards reliable clinical decision support

Asthma is a chronic respiratory disease with systemic and ocular manifestations. We present a multimodal deep-learning framework to predict asthma by fusing colour fundus photographs (CFP) with optical coherence tomography (OCT), and introduce a novel retinal dataset pairing fundus images with OCT measurements. The pipeline combined fundus CNN features with a feed-forward projection of OCT-derived optic nerve head (ONH) measurements for joint learning. Across five CNN backbones, fusion outperformed unimodal baselines; the best achieved AUC of 0.98, accuracy of 0.92, and F1 of 0.91. Findings supported retinal imaging as a noninvasive biomarker for systemic diseases (e.g., Parkinson, Alzheimer and cardiometabolic), constituting the first multimodal ocular-fusion approach to asthma diagnosis.